Evaluation of a Computerized Complex Instrumental Activities of Daily Living Marker (NMI)

Alzheimer Disease Clinical Trial

— AltoidaML  

Official title:

Evaluation of a Computerized Complex Instrumental Activities of Daily Living Marker (NMI) as a Pre-Clinical or Pro-Dromal Alzheimers Diagnosis (Prognosis) for Optimum Outcomes

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT02843529
• Other study ID #: altoidaML01
• Status: Completed
• Phase: N/A
• Start date: October 17, 2016
• Est. completion date: February 21, 2020

Study information

• Verified date: July 2023
• Source: Altoida
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

The proposed study is designed to evaluate the performance of the ALTOIDA™ System as a tool to assist physicians in diagnosing Alzheimer's Disease (AD) in real-world clinical settings. The design of this study is guided by two overriding factors: 1) to optimize the performance of the ALTOIDA™ Neuro Motor Index (NMI) prognosis classifiers, the subjects making up the training sets must be well characterized as to their clinical diagnosis, and 2) all ALTOIDA™ tests must be performed and reproduced in real-world clinical settings. Although there is already a large body of peer-reviewed scientific literature demonstrating that certain digital biomarker patterns are associated with certain neurologic conditions, the utilization of such tools for the evaluation of neurologic disorders is still considered an emerging science and therefore in the investigational stage. Although this protocol will report on brain patterns of certain neurologic conditions such as cognitive impairment and Alzheimer's disease, based on patterns published in peer-reviewed journals, such findings are not considered stand alone or diagnostic per se and should always be considered by the primary physician in conjunction with the patient's clinical condition. These data should only be used as additional information to add to the primary physician's diagnostic impression.

Description:

The goal of the study is to determine relationships among the clinical, cognitive, imaging, genetic, and biochemical biomarker characteristics of the stage of the AD spectrum that precedes MCI, the mildest symptomatic phase of AD, referred to here as MCI. The ADNI-GO model posits that AD begins with amyloid β (Aβ) deposition in the cortex, which leads to synaptic dysfunction, neurodegeneration, and cognitive/ functional decline. It may be possible to determine the future development of ALZ in a preclinical state in a cognitively normal but high risk individual at least 18-24 months before any symptoms develop of cognitive impairment. In addition a newly proposed research framework proposes to use biomarkers for amyloid, tau, and neurodegeneration (ATN) to classify MCI patients. Some of the leading-edge technologies under study are brain-imaging techniques, such as positron emission tomography (PET), including FDG-PET (which measures glucose metabolism in the brain); PET using a radioactive compound (F-AV-45) that measures brain beta-amyloid; and structural MRI. Brain scans are showing scientists how the brain's structure and function change as AD starts and progresses. Biomarkers in cerebrospinal fluid are revealing other changes that could identify which patients with MCI will develop Alzheimer's. Scientists are looking at levels of beta-amyloid and tau in cerebrospinal fluid. (Abnormal amounts of the amyloid and tau proteins in the brain are hallmarks of Alzheimer's disease.) The aim of the study is to evaluate the performance of the ALTOIDA™ System as as a tool to assist physicians in diagnosing Alzheimer's Disease (AD) in real-world clinical settings. The study will be : A. Multi-Center Study: primary goal of this study will be to evaluate the ALTOIDA™ Platform across multiple study locations. This will demonstrate an ability to perform tests, collect data, and generate classifications irrespective of variations in testing locations and personnel. 12 international study sites will be selected with the US based sites being a recognized NIH Center of Excellence for Alzheimer's disease or other nationally recognized Alzheimer's disease research center. Each site will evaluate up to 60 community dwellers evenly divided between MCI patients and age-matched controls (while the prevalence of AD is approximately 12% in the general population, the ratio of AD to normal among those who visit a clinic for memory or cognitive related issues is between 50-60%). Each site will follow the same testing protocols. Participants will be asked if they would like to participate in a protocol that monitors their prospective risk for developing ALZ short term, and whether certain of their prescribed medications may have a protective effect. Those who are accepting to be participants are then enrolled in the study. Enrollees will be tested for risk factors for having pre-clinical ALZ. Individuals identified as being at risk at baseline are followed at 6 month intervals for a 48 month period using psychometric testing and functional neuroimaging. Their maintenance of cognitive stability or cognitive decline is monitored while under the care of their PMD and while taking medications of interest. All test data will be uploaded to the online ALTOIDA™ database server. B. The overall impact of this study will be increased knowledge concerning the sequence and timing of events leading to MCI and from MCI to AD, development of better clinical and Neuro Motor Index prognosis methods for early detection and for monitoring the progression of these conditions, and facilitation of clinical trials of treatments to slow disease progression, ultimately contributing to the prevention of AD.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 548
• Est. completion date: February 21, 2020
• Est. primary completion date: February 18, 2020
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 55 Years to 90 Years

Eligibility

Inclusion Criteria:

• Between 55 and 90 years of age
• Study partner to accompany patient to all clinic visits for the duration of the protocol
• Memory complaint by patient and/or study partner
• Abnormal memory function score on Wechsler Memory Scale (adjusted for education)
• Mini-Mental State Exam score between 24 and 30 (inclusive)
• Clinical Dementia Rating = 0.5; Memory Box score at least 0.5
• General cognition and functional performance sufficiently preserved such that a diagnosis of Alzheimer's disease cannot be made by the site physician at the time of the screening visit
• Stability of the following permitted medications for 4 weeks (unless stated

otherwise):

• Antidepressants lacking significant anticholinergic side effects
• Estrogen replacement therapy
• Gingko biloba is permissible, but discouraged
• Washout from psychoactive medication (e.g., excluded antidepressants, neuroleptics, chronic anxiolytics or sedative hypnotics, etc.) for at least 4 weeks prior to screening
• Cholinesterase inhibitors and memantine if stable for 12 weeks prior to screening
• Geriatric Depression Scale less than 6
• Visual and auditory acuity adequate for neuropsychological testing
• Good general health with no diseases expected to interfere with the study
• Not pregnant, lactating, or of childbearing potential (i.e. women must be two years post-menopausal or surgically sterile)
• Hachinski less than or equal to 4
• Six grade education or has a good work history (sufficient to exclude mental retardation)
• Fluent in English or Spanish
• Agrees to at least one lumbar puncture for the collection of CSF
• Willing and able to complete all baseline assessments
• Willing to undergo repeated MRIs and at least two PET scans and willing to provide DNA and plasma samples as specified
• Willing and able to participate in a longitudinal imaging study

Exclusion Criteria:

• Any significant neurologic disease other than suspected incipient Alzheimer's disease, such as Parkinson's disease, multi-infarct dementia, Huntington's disease, normal pressure hydrocephalus, brain tumor, progressive supranuclear palsy, seizure disorder, subdural hematoma, multiple sclerosis, or history of significant head trauma followed by persistent neurologic defaults or known structural brain abnormalities
• Screening/baseline MRI scans with evidence of infection, infarction, or other focal lesions; multiple lacunes or lacunes in a critical memory structure
• Presence of pacemakers, aneurysm clips, artificial heart valves, ear implants, metal fragments or foreign objects in the eyes, skin or body
• Major depression, bipolar disorder as described in DSM-IV within the past 1 year
• Psychotic features, agitation or behavioral problems within the last 3 months which could lead to difficulty complying with the protocol
• History of schizophrenia
• History of alcohol or substance abuse or dependence within the past 2 years
• Any significant systemic illness or unstable medical condition which could lead to difficulty complying with the protocol
• Clinically significant abnormalities in B12, or TFTs that might interfere with the study
• Residence in skilled nursing facility
• Current use of specific psychoactive medications (e.g.,certain antidepressants, neuroleptics, chronic anxiolytics or sedative hypnotics, etc.); current use of warfarin (exclusionary for lumbar puncture)
• Use of investigational agents one month prior to entry and for the duration of the trial
• Exclusion for amyloid imaging with 18F -AV-45: Current or recent participation in any procedures involving radioactive agents such that the total radiation dose exposure to the participant in any given year would exceed the limits of annual and total dose commitment set forth in the US Code of Federal Regulations (CFR) Title 21 Section 361.1
• Exceptions to these guidelines may be considered on a case-by-case basis at the discretion of the protocol director

Related Conditions & MeSH terms

• Alzheimer Disease
• Asymptomatic Diseases
• Cognitive Dysfunction
• Cognitive Impairment
• Dementia
• Memory Disorders
• Mild Cognitive Impairment
• Presymptomatic Disease

Intervention

Other:
• Altoida: neuropsychological, MRI, EEG and CSF biomarkers
Data collection at baseline: clinical (neurological, activity of the daily life, instrumental activity of the daily life, depression scale), standard neuropsychological exam, ALTOIDA and neurophysiology (EEG/ERPs) in both Prodromal and Preclinical AD subjects. In both Prodromal and Preclinical AD subjects, APOE genotyping. The local clinical Unit should document the positivity at the baseline session of at least one of the biomarkers of AD mentioned above. Data collection at 6, 12, 24 and 36 months of follow up: clinical (neurological, activity of the daily life, instrumental activity of the daily life, depression scale), standard neuropsychological exam, ALTOIDA and neurophysiology (EEG/ERPs) in both Prodromal and Preclinical AD subjects.

Locations

Country	Name	City	State
n/a

Sponsors (16)

Lead Sponsor

Collaborator

Altoida

BiHELab - Bioinformatics and Human Electrophysiology Lab, Center for BrainHealth - The University of Texas at Dallas, EIT Health, Fundacion Clinic per a la Recerca Biomédica, Global Brain Health Institute (GBHI), Greek Alzheimer's Association and Related Disorders, IRCCS Centro San Giovanni di Dio Fatebenefratelli, Klinik Hirslanden, Zurich, Neuromed IRCCS, Research Center on Computational Biomarkers (RCCBM), Scripps Health, Takeda Pharmaceuticals International, Inc., University of Barcelona, University of Dublin, Trinity College, University of Roma La Sapienza

References & Publications (18)

Buegler M, Harms R, Balasa M, Meier IB, Exarchos T, Rai L, Boyle R, Tort A, Kozori M, Lazarou E, Rampini M, Cavaliere C, Vlamos P, Tsolaki M, Babiloni C, Soricelli A, Frisoni G, Sanchez-Valle R, Whelan R, Merlo-Pich E, Tarnanas I. Digital biomarker-based — View Citation

Cavedo E, Lista S, Khachaturian Z, Aisen P, Amouyel P, Herholz K, Jack CR Jr, Sperling R, Cummings J, Blennow K, O'Bryant S, Frisoni GB, Khachaturian A, Kivipelto M, Klunk W, Broich K, Andrieu S, de Schotten MT, Mangin JF, Lammertsma AA, Johnson K, Teipel S, Drzezga A, Bokde A, Colliot O, Bakardjian H, Zetterberg H, Dubois B, Vellas B, Schneider LS, Hampel H. The Road Ahead to Cure Alzheimer's Disease: Development of Biological Markers and Neuroimaging Methods for Prevention Trials Across all Stages and Target Populations. J Prev Alzheimers Dis. 2014 Dec;1(3):181-202. doi: 10.14283/jpad.2014.32. — View Citation

Christensen KD, Roberts JS, Whitehouse PJ, Royal CD, Obisesan TO, Cupples LA, Vernarelli JA, Bhatt DL, Linnenbringer E, Butson MB, Fasaye GA, Uhlmann WR, Hiraki S, Wang N, Cook-Deegan R, Green RC; REVEAL Study Group*. Disclosing Pleiotropic Effects During Genetic Risk Assessment for Alzheimer Disease: A Randomized Trial. Ann Intern Med. 2016 Feb 2;164(3):155-63. doi: 10.7326/M15-0187. Epub 2016 Jan 26. — View Citation

Dimitriadis SI, Laskaris NA, Bitzidou MP, Tarnanas I, Tsolaki MN. A novel biomarker of amnestic MCI based on dynamic cross-frequency coupling patterns during cognitive brain responses. Front Neurosci. 2015 Oct 20;9:350. doi: 10.3389/fnins.2015.00350. eCollection 2015. — View Citation

Galluzzi S, Marizzoni M, Babiloni C, Albani D, Antelmi L, Bagnoli C, Bartres-Faz D, Cordone S, Didic M, Farotti L, Fiedler U, Forloni G, Girtler N, Hensch T, Jovicich J, Leeuwis A, Marra C, Molinuevo JL, Nobili F, Pariente J, Parnetti L, Payoux P, Del Percio C, Ranjeva JP, Rolandi E, Rossini PM, Schonknecht P, Soricelli A, Tsolaki M, Visser PJ, Wiltfang J, Richardson JC, Bordet R, Blin O, Frisoni GB; PharmaCog Consortium. Clinical and biomarker profiling of prodromal Alzheimer's disease in workpackage 5 of the Innovative Medicines Initiative PharmaCog project: a 'European ADNI study'. J Intern Med. 2016 Jun;279(6):576-91. doi: 10.1111/joim.12482. Epub 2016 Mar 4. — View Citation

Green RC, Christensen KD, Cupples LA, Relkin NR, Whitehouse PJ, Royal CD, Obisesan TO, Cook-Deegan R, Linnenbringer E, Butson MB, Fasaye GA, Levinson E, Roberts JS; REVEAL Study Group. A randomized noninferiority trial of condensed protocols for genetic risk disclosure of Alzheimer's disease. Alzheimers Dement. 2015 Oct;11(10):1222-30. doi: 10.1016/j.jalz.2014.10.014. Epub 2014 Dec 9. — View Citation

Haller S, Nguyen D, Rodriguez C, Emch J, Gold G, Bartsch A, Lovblad KO, Giannakopoulos P. Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data. J Alzheimers Dis. 2010;22(1):315-27. doi: 10.3233/JAD-2010-100840. — View Citation

Lazarczyk MJ, Hof PR, Bouras C, Giannakopoulos P. Preclinical Alzheimer disease: identification of cases at risk among cognitively intact older individuals. BMC Med. 2012 Oct 25;10:127. doi: 10.1186/1741-7015-10-127. — View Citation

Nef T, Urwyler P, Buchler M, Tarnanas I, Stucki R, Cazzoli D, Muri R, Mosimann U. Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data. Sensors (Basel). 2015 May 21;15(5):11725-40. doi: 10.3390/s150511725. — View Citation

Rizk-Jackson A, Insel P, Petersen R, Aisen P, Jack C, Weiner M. Early indications of future cognitive decline: stable versus declining controls. PLoS One. 2013 Sep 9;8(9):e74062. doi: 10.1371/journal.pone.0074062. eCollection 2013. — View Citation

Tarnanas I, Laskaris N, Tsolaki M, Muri R, Nef T, Mosimann UP. On the comparison of a novel serious game and electroencephalography biomarkers for early dementia screening. Adv Exp Med Biol. 2015;821:63-77. doi: 10.1007/978-3-319-08939-3_11. — View Citation

Tarnanas I, Papagiannopoulos S, Kazis D, Wiederhold M, Widerhold B, Tsolaki M. Reliability of a novel serious game using dual-task gait profiles to early characterize aMCI. Front Aging Neurosci. 2015 Apr 22;7:50. doi: 10.3389/fnagi.2015.00050. eCollection 2015. — View Citation

Tarnanas I, Schlee W, Tsolaki M, Muri R, Mosimann U, Nef T. Ecological validity of virtual reality daily living activities screening for early dementia: longitudinal study. JMIR Serious Games. 2013 Aug 6;1(1):e1. doi: 10.2196/games.2778. — View Citation

Tarnanas I, Tsolaki A, Wiederhold M, Wiederhold B, Tsolaki M. Five-year biomarker progression variability for Alzheimer's disease dementia prediction: Can a complex instrumental activities of daily living marker fill in the gaps? Alzheimers Dement (Amst). 2015 Nov 14;1(4):521-32. doi: 10.1016/j.dadm.2015.10.005. eCollection 2015 Dec. — View Citation

Tarnanas I, Tsolaki M, Nef T, M Muri R, Mosimann UP. Can a novel computerized cognitive screening test provide additional information for early detection of Alzheimer's disease? Alzheimers Dement. 2014 Nov;10(6):790-8. doi: 10.1016/j.jalz.2014.01.002. Epub 2014 Mar 18. — View Citation

Vallejo V, Mitache AV, Tarnanas I, Muri R, Mosimann UP, Nef T. Combining qualitative and quantitative methods to analyze serious games outcomes: A pilot study for a new cognitive screening tool. Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:1327-30. doi: 10.1109/EMBC.2015.7318613. — View Citation

Woodard JL, Seidenberg M, Nielson KA, Smith JC, Antuono P, Durgerian S, Guidotti L, Zhang Q, Butts A, Hantke N, Lancaster M, Rao SM. Prediction of cognitive decline in healthy older adults using fMRI. J Alzheimers Dis. 2010;21(3):871-85. doi: 10.3233/JAD-2010-091693. — View Citation

Xekardaki A, Rodriguez C, Montandon ML, Toma S, Tombeur E, Herrmann FR, Zekry D, Lovblad KO, Barkhof F, Giannakopoulos P, Haller S. Arterial spin labeling may contribute to the prediction of cognitive deterioration in healthy elderly individuals. Radiology. 2015 Feb;274(2):490-9. doi: 10.1148/radiol.14140680. Epub 2014 Oct 7. — View Citation

* Note: There are 18 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Change in Diagnostic Area Under the Receiver Operating Characteristic Curve (ROC-AUC)	The machine learning models capturing voice data, hands micromovements & micro-errors, posture changes, eye tracking, visuospatial navigation micro-errors and spatio-temporal gait parameters developed for the Altoida system will be tested in this prospective cohort. Sensitivity, specificity and accuracy of the model will be tested in differential diagnosis between the study groups as well as the accuracy of prediction cognitive decline as measured by neuropsychological test battery in the MCI group.	approximately 40 months follow up
Secondary	Change From Baseline in Clinical Measure 1	Clinical Dementia Rating (CDR), including CDR sum of boxes (CDR-SB) and clinician's diagnostic assessment	baseline, 6, 12, 24, 36 and 42 months of follow up
Secondary	Change From Baseline in Clinical Measure 2	Geriatric Depression Scale (GDS)	baseline, 6, 12, 24, 36 and approximately 40 months follow up
Secondary	Change From Baseline in Clinical Measure 3	Functional Assessment Questionnaire (FAQ)	baseline, 6, 12, 24, 36 and approximately 40 months follow up
Secondary	Change From Baseline in Clinical Measure 4	Mini Mental Status Exam (MMSE)	baseline, 6, 12, 24, 36 and approximately 40 months follow up
Secondary	Change From Baseline in Clinical Measure 5	Neuropsychiatric Inventory Questionnaire (NPI-Q)	baseline, 6, 12, 24, 36 and approximately 40 months follow up
Secondary	Change From Baseline in Clinical Measure 6	Activities of the daily life (ADL)	baseline, 6, 12, 24, 36 and approximately 40 months follow up
Secondary	Change From Baseline in Clinical Measure 7	Instrumental activities of the daily life (iADL)	baseline, 6, 12, 24, 36 and approximately 40 months follow up
Secondary	Change From Baseline in Cognitive Measure	ADAS Cog	baseline, 6, 12, 24, 36 and approximately 40 months follow up
Secondary	Change From Baseline in Cognitive Measure	-Rey-Osterrieth Complex Figure Test (Copy)	baseline, 6, 12, 24, 36 and approximately 40 months follow up
Secondary	Change From Baseline in Cognitive Measure	Trail Making Test	baseline, 6, 12, 24, 36 and approximately 40 months follow up
Secondary	Change From Baseline in Cognitive Measure	Digit Span Forward	baseline, 6, 12, 24, 36 and approximately 40 months follow up
Secondary	Change From Baseline in Cognitive Measure	Category Fluency (Animals & Vegetables)	baseline, 6, 12, 24, 36 and approximately 40 months follow up
Secondary	Change From Baseline in Cognitive Measure	Digit Span Backward	baseline, 6, 12, 24, 36 and approximately 40 months follow up
Secondary	Change From Baseline in Cognitive Measure	Rey Osterrieth Complex Figure Test (30 minute delay)	baseline, 6, 12, 24, 36 and approximately 40 months follow up
Secondary	Change From Baseline in Cognitive Measure	Wechsler Memory Scale - Revised (WMS-R) Digit Span	baseline, 6, 12, 24, 36 and approximately 40 months follow up
Secondary	Change From Baseline in Cognitive Measure	Wechsler Memory Scale Logical Memory	baseline, 6, 12, 24, 36 and approximately 40 months follow up
Secondary	Change From Baseline in Cognitive Measure	Wechsler Memory Scale Paragraph Memory (Immediate & Delayed Recall)	baseline, 6, 12, 24, 36 and approximately 40 months follow up
Secondary	Change From Baseline in Cognitive Measure	Wechsler Adult Intelligence Scale - Revised (WAIS-R) Digit-Symbol Substitution Test	baseline, 6, 12, 24, 36 and approximately 40 months follow up
Secondary	Change From Baseline in Cognitive Measure	Rey Auditory Verbal Learning Test (RAVLT)	baseline, 6, 12, 24, 36 and approximately 40 months follow up
Secondary	Secondary Resting State EEG Endpoints	EEG endpoints (occipital, parietal, and temporal sources of delta and low-frequency alpha rhythms) according to the PharmaCog WP5 European ADNI. These markers are expected to be related to disease status at baseline assessment and disease progression at follow-ups. Exploratory probability level of p < 0.05.	baseline, 6, 12, 24, 36 and approximately 40 months follow up
Secondary	Secondary Resting State Auditory Oddball ERP Endpoints	ERP endpoints (latency of scalp parietal P3b peak and activity of the cingulate and temporal-parietal sources of P3b peak according to PharmaCog WP5 European ADNI). These markers are expected to be related to disease status at baseline assessment and disease progression at follow-ups. Exploratory probability level of p < 0.05.	baseline, 6, 12, 24, 36 and approximately 40 months follow up
Secondary	Total Abeta 1-42 (Aß42) Amyloid Deposition	Currently available evidence strongly supports the position that the initiating event in Alzheimer's disease (AD) is related to abnormal processing of beta-amyloid (Abeta) peptide, ultimately leading to formation of Abeta plaques in the brain. Baseline amount of CSF Abeta(42) will be investigated.	baseline, 6, 12, 24, 36 and approximately 40 months follow up
Secondary	Change of Brain Amyloid Deposition	Biomarkers of brain beta-amyloidosis are reductions in CSF Abeta(42) and increased amyloid PET tracer retention. The change in amyloid deposition as measured by Abeta 1-42 (Aß42) and its relation to the genetic, clinical, neuropsychological, EEG and ERP endpoints measurement will be assessed.	baseline, 6, 12, 24, 36 and approximately 40 months follow up
Secondary	Change of CSF Biomarkers Tau and ptau181 Values	The change in CSF biomarkers tau and ptau181 values and its relation to the genetic, clinical, neuropsychological, EEG and ERP endpoints measurement will be assessed.	baseline, 6, 12, 24, 36 and approximately 40 months follow up
Secondary	MRI (Optional)	Relationship between MRI measures (brain volume, hippocampus atrophy, vascular lesions) and biomarkers.	baseline, 6, 12, 24, 36 and approximately 40 months follow up
Secondary	Changes in Driving Breaking Force	Changes in driving behavior, such as breaking force observed continuesly through in-car sensors or dongles.	baseline, 6, 12, 24, 36 and approximately 40 months follow up
Secondary	Changes in Driving Acceleration Velocity	Changes in driving behavior, such as acceleration velocity observed continuesly through in-car sensors or dongles.	baseline, 6, 12, 24, 36 and approximately 40 months follow up
Secondary	Changes in Driving Direction	Changes in driving behavior, such as sudden changes of direction observed continuesly through in-car sensors or dongles.	baseline, 6, 12, 24, 36 and approximately 40 months follow up
Secondary	Changes in Driving Violations	Changes in driving behavior, such as speed limit violations observed continuesly through in-car sensors or dongles.	Continuous measurement for approximately 12 months follow up